{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------\n",
    "*日期：2023-03-8\\ week03（周三）\n",
    "*课程：Python\n",
    "*记录人：Xiao lu\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# series\n",
    "# DataFrame\n",
    "## 常用基本函数\n",
    "\n",
    "1.汇总函数\n",
    "\n",
    "2.特征统计函数\n",
    "\n",
    "3.唯一值函数\n",
    "\n",
    "4.替换函数\n",
    "\n",
    "5.排序函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(\n",
    "    data = [100,'a',{'dict1',5}],\n",
    "    index = pd.Index([1,2,3],name='my_idx'),\n",
    "    dtype = 'object',\n",
    "    name = 'my_name'\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "my_idx\n",
       "1           100\n",
       "2             a\n",
       "3    {dict1, 5}\n",
       "Name: my_name, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series(\n",
    "    data = [67,78,75],\n",
    "    index = pd.Index(['数学','语文','英语'],name='学科')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "学科\n",
       "数学    67\n",
       "语文    78\n",
       "英语    75\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([67, 78, 75], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['数学', '语文', '英语'], dtype='object', name='学科')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series(\n",
    "    data = [67,78,75],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    67\n",
       "1    78\n",
       "2    75\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrame\n",
    "\n",
    "* 具有相同特征和个数的列表数据的集合，可以用DataFrame来描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "    [1, 'a', 1.2],\n",
    "    [2, 'b', 2.2], \n",
    "    [3, 'c', 3.2]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    data = data,\n",
    "    index = ['row_0','row_1','row_2'],\n",
    "    columns = ['col_0','col_1','col_2']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col_0 col_1  col_2\n",
       "row_0      1     a    1.2\n",
       "row_1      2     b    2.2\n",
       "row_2      3     c    3.2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    'col_0':[1,2,3],\n",
    "    'col_1':['a','b','c'],\n",
    "    'col_2':[1.2,2.2,3.2]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    data = data,\n",
    "    index = ['row_0','row_1','row_2']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col_0 col_1  col_2\n",
       "row_0      1     a    1.2\n",
       "row_1      2     b    2.2\n",
       "row_2      3     c    3.2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrame 取值的一般方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_0    1\n",
       "row_1    2\n",
       "row_2    3\n",
       "Name: col_0, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_0</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col_0  col_2\n",
       "row_0      1    1.2\n",
       "row_1      2    2.2\n",
       "row_2      3    3.2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['col_0','col_2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* iloc ：强大的切片取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col_1</th>\n",
       "      <th>col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row_1</th>\n",
       "      <td>b</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_2</th>\n",
       "      <td>c</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col_1  col_2\n",
       "row_1     b    2.2\n",
       "row_2     c    3.2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 课后练习(参考 pandas cheat sheet)：\n",
    "\n",
    "> 1.iloc\n",
    "> 2.loc\n",
    "> 3.iat\n",
    "> 4.at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 'a', 1.2],\n",
       "       [2, 'b', 2.2],\n",
       "       [3, 'c', 3.2]], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['row_0', 'row_1', 'row_2'], dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['col_0', 'col_1', 'col_2'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 常用基本函数\n",
    "1.汇总函数\n",
    "\n",
    "2.特征统计函数\n",
    "\n",
    "3.唯一值函数\n",
    "\n",
    "4.替换函数\n",
    "\n",
    "5.排序函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaoli Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/31</td>\n",
       "      <td>0:03:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/12</td>\n",
       "      <td>0:03:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoqiang Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/3</td>\n",
       "      <td>0:03:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changli Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>163.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Juan Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaopeng Zhou</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/29</td>\n",
       "      <td>0:05:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaoquan Lv</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/16</td>\n",
       "      <td>0:04:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Peng You</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/20</td>\n",
       "      <td>0:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Yanfeng Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/19</td>\n",
       "      <td>0:05:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaomei Zhou</td>\n",
       "      <td>Female</td>\n",
       "      <td>165.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/29</td>\n",
       "      <td>0:05:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/1</td>\n",
       "      <td>0:05:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaoqiang Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>170.1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/11</td>\n",
       "      <td>0:04:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Peng Wang</td>\n",
       "      <td>Male</td>\n",
       "      <td>162.8</td>\n",
       "      <td>65.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/2</td>\n",
       "      <td>0:04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaofeng Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>170.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/4</td>\n",
       "      <td>0:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Qiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/30</td>\n",
       "      <td>0:03:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Changjuan You</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaopeng Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/2</td>\n",
       "      <td>0:04:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Changqiang Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>166.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/29</td>\n",
       "      <td>0:05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Qiang Zheng</td>\n",
       "      <td>Male</td>\n",
       "      <td>183.9</td>\n",
       "      <td>87.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/5</td>\n",
       "      <td>0:04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chunmei You</td>\n",
       "      <td>Male</td>\n",
       "      <td>167.4</td>\n",
       "      <td>69.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/17</td>\n",
       "      <td>0:04:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaopeng Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>156.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/19</td>\n",
       "      <td>0:04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Yanli You</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/23</td>\n",
       "      <td>0:03:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Qiang Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>163.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/11</td>\n",
       "      <td>0:05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaoqiang Qin</td>\n",
       "      <td>Female</td>\n",
       "      <td>170.2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Changmei Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Li Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>165.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/26</td>\n",
       "      <td>0:04:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaofeng Zhang</td>\n",
       "      <td>Male</td>\n",
       "      <td>176.4</td>\n",
       "      <td>80.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/25</td>\n",
       "      <td>0:05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Quan Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.6</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/4</td>\n",
       "      <td>0:03:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaojuan Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.8</td>\n",
       "      <td>44.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/26</td>\n",
       "      <td>0:03:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaopeng Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/4</td>\n",
       "      <td>0:05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Yanli Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/8</td>\n",
       "      <td>0:03:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaopeng Zhou</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/15</td>\n",
       "      <td>0:04:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoqiang Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>167.1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/11</td>\n",
       "      <td>0:04:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Li Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:05:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Peng Wang</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/3</td>\n",
       "      <td>0:05:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Zheng</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/28</td>\n",
       "      <td>0:03:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Yanjuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/23</td>\n",
       "      <td>0:03:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaoqiang Qian</td>\n",
       "      <td>Male</td>\n",
       "      <td>170.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/3</td>\n",
       "      <td>0:04:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaofeng Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>159.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:05:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Feng</td>\n",
       "      <td>Male</td>\n",
       "      <td>178.9</td>\n",
       "      <td>80.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/6</td>\n",
       "      <td>0:04:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Chunmei Wang</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/10</td>\n",
       "      <td>0:04:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Yanjuan Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/9</td>\n",
       "      <td>0:04:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/11</td>\n",
       "      <td>0:05:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaopeng Shen</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/16</td>\n",
       "      <td>0:03:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Chunjuan Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/18</td>\n",
       "      <td>0:05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Changli Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>177.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/21</td>\n",
       "      <td>0:03:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Li Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>166.6</td>\n",
       "      <td>54.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/3</td>\n",
       "      <td>0:04:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Gaojuan Wang</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.8</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/23</td>\n",
       "      <td>0:03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaoqiang Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>193.9</td>\n",
       "      <td>79.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Yanmei Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/3</td>\n",
       "      <td>0:05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:04:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/22</td>\n",
       "      <td>0:04:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>Male</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School      Grade             Name  Gender  \\\n",
       "0    Shanghai Jiao Tong University   Freshman     Gaopeng Yang  Female   \n",
       "1                Peking University   Freshman   Changqiang You    Male   \n",
       "2    Shanghai Jiao Tong University     Senior          Mei Sun    Male   \n",
       "3                 Fudan University  Sophomore     Xiaojuan Sun  Female   \n",
       "4                 Fudan University  Sophomore      Gaojuan You    Male   \n",
       "5              Tsinghua University   Freshman      Xiaoli Qian  Female   \n",
       "6    Shanghai Jiao Tong University   Freshman        Qiang Chu  Female   \n",
       "7              Tsinghua University     Junior    Gaoqiang Qian  Female   \n",
       "8              Tsinghua University   Freshman    Changli Zhang  Female   \n",
       "9                Peking University     Junior          Juan Xu  Female   \n",
       "10   Shanghai Jiao Tong University   Freshman    Xiaopeng Zhou    Male   \n",
       "11             Tsinghua University     Junior      Xiaoquan Lv  Female   \n",
       "12   Shanghai Jiao Tong University     Senior         Peng You  Female   \n",
       "13   Shanghai Jiao Tong University  Sophomore     Yanfeng Qian  Female   \n",
       "14             Tsinghua University     Senior     Xiaomei Zhou  Female   \n",
       "15                Fudan University   Freshman  Changqiang Yang  Female   \n",
       "16             Tsinghua University     Junior    Xiaoqiang Qin    Male   \n",
       "17             Tsinghua University     Junior        Peng Wang    Male   \n",
       "18             Tsinghua University     Senior     Xiaofeng Sun    Male   \n",
       "19   Shanghai Jiao Tong University     Senior        Qiang Chu  Female   \n",
       "20               Peking University     Junior    Changjuan You  Female   \n",
       "21   Shanghai Jiao Tong University     Senior    Xiaopeng Shen    Male   \n",
       "22   Shanghai Jiao Tong University     Senior   Changqiang Sun  Female   \n",
       "23   Shanghai Jiao Tong University     Senior      Qiang Zheng    Male   \n",
       "24             Tsinghua University     Senior      Chunmei You    Male   \n",
       "25             Tsinghua University     Senior     Xiaopeng Chu  Female   \n",
       "26                Fudan University     Junior        Yanli You  Female   \n",
       "27             Tsinghua University     Junior        Qiang Sun  Female   \n",
       "28                Fudan University   Freshman     Gaoqiang Qin  Female   \n",
       "29               Peking University  Sophomore      Changmei Xu  Female   \n",
       "..                             ...        ...              ...     ...   \n",
       "170               Fudan University  Sophomore           Li Sun  Female   \n",
       "171  Shanghai Jiao Tong University     Senior   Xiaofeng Zhang    Male   \n",
       "172  Shanghai Jiao Tong University     Junior        Quan Zhao  Female   \n",
       "173               Fudan University     Junior     Gaojuan Qian  Female   \n",
       "174  Shanghai Jiao Tong University     Junior     Xiaopeng Sun  Female   \n",
       "175            Tsinghua University     Senior      Yanli Zhang  Female   \n",
       "176            Tsinghua University     Junior    Xiaopeng Zhou  Female   \n",
       "177            Tsinghua University     Junior     Gaoqiang Qin    Male   \n",
       "178            Tsinghua University  Sophomore           Li Qin    Male   \n",
       "179            Tsinghua University     Senior        Peng Wang    Male   \n",
       "180            Tsinghua University     Senior        Mei Zheng  Female   \n",
       "181            Tsinghua University  Sophomore      Yanjuan You    Male   \n",
       "182            Tsinghua University  Sophomore   Xiaoqiang Qian    Male   \n",
       "183              Peking University     Junior    Xiaofeng Zhao  Female   \n",
       "184  Shanghai Jiao Tong University   Freshman       Qiang Feng    Male   \n",
       "185              Peking University   Freshman     Chunmei Wang  Female   \n",
       "186               Fudan University   Freshman     Yanjuan Zhao  Female   \n",
       "187               Fudan University     Junior    Xiaojuan Qian  Female   \n",
       "188  Shanghai Jiao Tong University     Junior    Xiaopeng Shen  Female   \n",
       "189               Fudan University     Junior   Chunjuan Zhang  Female   \n",
       "190  Shanghai Jiao Tong University     Junior      Changli Qin    Male   \n",
       "191            Tsinghua University     Junior           Li Sun  Female   \n",
       "192  Shanghai Jiao Tong University     Senior     Gaojuan Wang    Male   \n",
       "193            Tsinghua University     Senior    Xiaoqiang Qin    Male   \n",
       "194              Peking University     Senior      Yanmei Qian  Female   \n",
       "195               Fudan University     Junior     Xiaojuan Sun  Female   \n",
       "196            Tsinghua University     Senior          Li Zhao  Female   \n",
       "197  Shanghai Jiao Tong University     Senior   Chengqiang Chu  Female   \n",
       "198  Shanghai Jiao Tong University     Senior    Chengmei Shen    Male   \n",
       "199            Tsinghua University  Sophomore      Chunpeng Lv    Male   \n",
       "\n",
       "     Height  Weight Transfer  Test_Number   Test_Date Time_Record  \n",
       "0     158.9    46.0        N            1   2019/10/5     0:04:34  \n",
       "1     166.5    70.0        N            1    2019/9/4     0:04:20  \n",
       "2     188.9    89.0        N            2   2019/9/12     0:05:22  \n",
       "3       NaN    41.0        N            2    2020/1/3     0:04:08  \n",
       "4     174.0    74.0        N            2   2019/11/6     0:05:22  \n",
       "5     158.0    51.0        N            1  2019/10/31     0:03:47  \n",
       "6     162.5    52.0        N            1  2019/12/12     0:03:53  \n",
       "7     161.9    50.0        N            1    2019/9/3     0:03:45  \n",
       "8     163.0    48.0        N            1    2020/1/5     0:05:13  \n",
       "9     164.8     NaN        N            3   2019/10/5     0:04:05  \n",
       "10    174.1    74.0        N            1   2019/9/29     0:05:16  \n",
       "11    153.2    43.0        N            2   2019/9/16     0:04:49  \n",
       "12      NaN    48.0      NaN            2  2019/10/20     0:04:10  \n",
       "13    160.1    48.0        N            2   2019/9/19     0:05:29  \n",
       "14    165.3    57.0        N            1  2019/12/29     0:05:25  \n",
       "15    156.0    49.0        N            3    2020/1/1     0:05:25  \n",
       "16    170.1    68.0        N            1   2019/9/11     0:04:51  \n",
       "17    162.8    65.0        N            1   2019/11/2     0:04:53  \n",
       "18    170.3    71.0        N            2   2019/11/4     0:03:32  \n",
       "19    162.4    50.0        N            3   2019/9/30     0:03:36  \n",
       "20    161.4    47.0        N            1   2019/10/5     0:04:08  \n",
       "21    166.0    62.0      NaN            1    2020/1/2     0:04:54  \n",
       "22    166.1    55.0        N            1  2019/11/29     0:05:01  \n",
       "23    183.9    87.0        N            1   2019/12/5     0:04:59  \n",
       "24    167.4    69.0        N            1  2019/11/17     0:04:32  \n",
       "25    156.5    42.0        N            1  2019/11/19     0:04:59  \n",
       "26      NaN    48.0        N            1   2019/9/23     0:03:34  \n",
       "27    163.1    53.0        N            1  2019/12/11     0:05:08  \n",
       "28    170.2    63.0        N            2    2020/1/7     0:05:24  \n",
       "29    151.6    43.0        N            2    2020/1/3     0:04:28  \n",
       "..      ...     ...      ...          ...         ...         ...  \n",
       "170   165.1    57.0        N            2  2019/12/26     0:04:57  \n",
       "171   176.4    80.0        N            1  2019/12/25     0:05:03  \n",
       "172   160.6    53.0        N            2   2019/10/4     0:03:45  \n",
       "173   154.8    44.0        N            3  2019/10/26     0:03:47  \n",
       "174   161.9    54.0        N            2   2019/11/4     0:05:09  \n",
       "175   154.2    41.0        N            3    2020/1/8     0:03:48  \n",
       "176   160.2    51.0      NaN            2  2019/11/15     0:04:57  \n",
       "177   167.1    71.0        N            2  2019/10/11     0:04:14  \n",
       "178     NaN    76.0        N            3    2020/1/7     0:05:19  \n",
       "179   175.5    73.0        N            3   2019/10/3     0:05:14  \n",
       "180   161.1    50.0        N            3  2019/10/28     0:03:42  \n",
       "181     NaN    55.0        N            1  2019/11/23     0:03:50  \n",
       "182   170.5    73.0        N            3   2019/10/3     0:04:11  \n",
       "183   159.9    46.0        N            1  2019/10/17     0:05:20  \n",
       "184   178.9    80.0        N            2   2019/12/6     0:04:23  \n",
       "185   151.2    43.0        N            2  2019/12/10     0:04:24  \n",
       "186     NaN    53.0        N            2   2019/10/9     0:04:21  \n",
       "187   164.7    51.0        N            2  2019/10/11     0:05:11  \n",
       "188   160.1    53.0        N            1  2019/10/16     0:03:33  \n",
       "189   158.9    47.0        N            2   2019/9/18     0:05:09  \n",
       "190   177.3     NaN        N            1  2019/11/21     0:03:57  \n",
       "191   166.6    54.0        N            2    2019/9/3     0:04:45  \n",
       "192   166.8    70.0        N            1  2019/12/23     0:03:54  \n",
       "193   193.9    79.0        N            2   2019/11/6     0:05:09  \n",
       "194   160.3    49.0      NaN            1   2019/12/3     0:05:08  \n",
       "195   153.9    46.0        N            2  2019/10/17     0:04:31  \n",
       "196   160.9    50.0        N            3   2019/9/22     0:04:03  \n",
       "197   153.9    45.0        N            1    2020/1/5     0:04:48  \n",
       "198   175.3    71.0        N            2    2020/1/7     0:04:58  \n",
       "199   155.7    51.0        N            1   2019/11/6     0:05:05  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/learn_pandas.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['School', 'Grade', 'Name', 'Gender', 'Height', 'Weight', 'Transfer',\n",
       "       'Test_Number', 'Test_Date', 'Time_Record'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Xiaoli Qian</td>\n",
       "      <td>158.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Qiang Chu</td>\n",
       "      <td>162.5</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaoqiang Qian</td>\n",
       "      <td>161.9</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Changli Zhang</td>\n",
       "      <td>163.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Juan Xu</td>\n",
       "      <td>164.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Xiaopeng Zhou</td>\n",
       "      <td>174.1</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Xiaoquan Lv</td>\n",
       "      <td>153.2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Peng You</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Yanfeng Qian</td>\n",
       "      <td>160.1</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Xiaomei Zhou</td>\n",
       "      <td>165.3</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Changqiang Yang</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Xiaoqiang Qin</td>\n",
       "      <td>170.1</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Peng Wang</td>\n",
       "      <td>162.8</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Xiaofeng Sun</td>\n",
       "      <td>170.3</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Qiang Chu</td>\n",
       "      <td>162.4</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Changjuan You</td>\n",
       "      <td>161.4</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Xiaopeng Shen</td>\n",
       "      <td>166.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Changqiang Sun</td>\n",
       "      <td>166.1</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Qiang Zheng</td>\n",
       "      <td>183.9</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Chunmei You</td>\n",
       "      <td>167.4</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Xiaopeng Chu</td>\n",
       "      <td>156.5</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Yanli You</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Qiang Sun</td>\n",
       "      <td>163.1</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Gaoqiang Qin</td>\n",
       "      <td>170.2</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Changmei Xu</td>\n",
       "      <td>151.6</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Li Sun</td>\n",
       "      <td>165.1</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Xiaofeng Zhang</td>\n",
       "      <td>176.4</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Quan Zhao</td>\n",
       "      <td>160.6</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Gaojuan Qian</td>\n",
       "      <td>154.8</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Xiaopeng Sun</td>\n",
       "      <td>161.9</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Yanli Zhang</td>\n",
       "      <td>154.2</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Xiaopeng Zhou</td>\n",
       "      <td>160.2</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Gaoqiang Qin</td>\n",
       "      <td>167.1</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Li Qin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Peng Wang</td>\n",
       "      <td>175.5</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Mei Zheng</td>\n",
       "      <td>161.1</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Yanjuan You</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Xiaoqiang Qian</td>\n",
       "      <td>170.5</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Xiaofeng Zhao</td>\n",
       "      <td>159.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Qiang Feng</td>\n",
       "      <td>178.9</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Chunmei Wang</td>\n",
       "      <td>151.2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Yanjuan Zhao</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Xiaojuan Qian</td>\n",
       "      <td>164.7</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Xiaopeng Shen</td>\n",
       "      <td>160.1</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Chunjuan Zhang</td>\n",
       "      <td>158.9</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Changli Qin</td>\n",
       "      <td>177.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Li Sun</td>\n",
       "      <td>166.6</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Gaojuan Wang</td>\n",
       "      <td>166.8</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Xiaoqiang Qin</td>\n",
       "      <td>193.9</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Yanmei Qian</td>\n",
       "      <td>160.3</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Height  Weight\n",
       "0       Gaopeng Yang   158.9    46.0\n",
       "1     Changqiang You   166.5    70.0\n",
       "2            Mei Sun   188.9    89.0\n",
       "3       Xiaojuan Sun     NaN    41.0\n",
       "4        Gaojuan You   174.0    74.0\n",
       "5        Xiaoli Qian   158.0    51.0\n",
       "6          Qiang Chu   162.5    52.0\n",
       "7      Gaoqiang Qian   161.9    50.0\n",
       "8      Changli Zhang   163.0    48.0\n",
       "9            Juan Xu   164.8     NaN\n",
       "10     Xiaopeng Zhou   174.1    74.0\n",
       "11       Xiaoquan Lv   153.2    43.0\n",
       "12          Peng You     NaN    48.0\n",
       "13      Yanfeng Qian   160.1    48.0\n",
       "14      Xiaomei Zhou   165.3    57.0\n",
       "15   Changqiang Yang   156.0    49.0\n",
       "16     Xiaoqiang Qin   170.1    68.0\n",
       "17         Peng Wang   162.8    65.0\n",
       "18      Xiaofeng Sun   170.3    71.0\n",
       "19         Qiang Chu   162.4    50.0\n",
       "20     Changjuan You   161.4    47.0\n",
       "21     Xiaopeng Shen   166.0    62.0\n",
       "22    Changqiang Sun   166.1    55.0\n",
       "23       Qiang Zheng   183.9    87.0\n",
       "24       Chunmei You   167.4    69.0\n",
       "25      Xiaopeng Chu   156.5    42.0\n",
       "26         Yanli You     NaN    48.0\n",
       "27         Qiang Sun   163.1    53.0\n",
       "28      Gaoqiang Qin   170.2    63.0\n",
       "29       Changmei Xu   151.6    43.0\n",
       "..               ...     ...     ...\n",
       "170           Li Sun   165.1    57.0\n",
       "171   Xiaofeng Zhang   176.4    80.0\n",
       "172        Quan Zhao   160.6    53.0\n",
       "173     Gaojuan Qian   154.8    44.0\n",
       "174     Xiaopeng Sun   161.9    54.0\n",
       "175      Yanli Zhang   154.2    41.0\n",
       "176    Xiaopeng Zhou   160.2    51.0\n",
       "177     Gaoqiang Qin   167.1    71.0\n",
       "178           Li Qin     NaN    76.0\n",
       "179        Peng Wang   175.5    73.0\n",
       "180        Mei Zheng   161.1    50.0\n",
       "181      Yanjuan You     NaN    55.0\n",
       "182   Xiaoqiang Qian   170.5    73.0\n",
       "183    Xiaofeng Zhao   159.9    46.0\n",
       "184       Qiang Feng   178.9    80.0\n",
       "185     Chunmei Wang   151.2    43.0\n",
       "186     Yanjuan Zhao     NaN    53.0\n",
       "187    Xiaojuan Qian   164.7    51.0\n",
       "188    Xiaopeng Shen   160.1    53.0\n",
       "189   Chunjuan Zhang   158.9    47.0\n",
       "190      Changli Qin   177.3     NaN\n",
       "191           Li Sun   166.6    54.0\n",
       "192     Gaojuan Wang   166.8    70.0\n",
       "193    Xiaoqiang Qin   193.9    79.0\n",
       "194      Yanmei Qian   160.3    49.0\n",
       "195     Xiaojuan Sun   153.9    46.0\n",
       "196          Li Zhao   160.9    50.0\n",
       "197   Chengqiang Chu   153.9    45.0\n",
       "198    Chengmei Shen   175.3    71.0\n",
       "199      Chunpeng Lv   155.7    51.0\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Name','Height','Weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          School      Grade            Name  Gender  Height  \\\n",
       "0  Shanghai Jiao Tong University   Freshman    Gaopeng Yang  Female   158.9   \n",
       "1              Peking University   Freshman  Changqiang You    Male   166.5   \n",
       "2  Shanghai Jiao Tong University     Senior         Mei Sun    Male   188.9   \n",
       "3               Fudan University  Sophomore    Xiaojuan Sun  Female     NaN   \n",
       "4               Fudan University  Sophomore     Gaojuan You    Male   174.0   \n",
       "\n",
       "   Weight Transfer  Test_Number  Test_Date Time_Record  \n",
       "0    46.0        N            1  2019/10/5     0:04:34  \n",
       "1    70.0        N            1   2019/9/4     0:04:20  \n",
       "2    89.0        N            2  2019/9/12     0:05:22  \n",
       "3    41.0        N            2   2020/1/3     0:04:08  \n",
       "4    74.0        N            2  2019/11/6     0:05:22  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 10 columns):\n",
      "School         200 non-null object\n",
      "Grade          200 non-null object\n",
      "Name           200 non-null object\n",
      "Gender         200 non-null object\n",
      "Height         183 non-null float64\n",
      "Weight         189 non-null float64\n",
      "Transfer       188 non-null object\n",
      "Test_Number    200 non-null int64\n",
      "Test_Date      200 non-null object\n",
      "Time_Record    200 non-null object\n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 15.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Test_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>163.218033</td>\n",
       "      <td>55.015873</td>\n",
       "      <td>1.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.608879</td>\n",
       "      <td>12.824294</td>\n",
       "      <td>0.722207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>145.400000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>157.150000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>161.900000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>167.500000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>193.900000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height      Weight  Test_Number\n",
       "count  183.000000  189.000000   200.000000\n",
       "mean   163.218033   55.015873     1.645000\n",
       "std      8.608879   12.824294     0.722207\n",
       "min    145.400000   34.000000     1.000000\n",
       "25%    157.150000   46.000000     1.000000\n",
       "50%    161.900000   51.000000     1.500000\n",
       "75%    167.500000   65.000000     2.000000\n",
       "max    193.900000   89.000000     3.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征统计函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>158.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>162.5</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>161.9</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>163.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>164.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>174.1</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>153.2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>160.1</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>165.3</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>170.1</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>162.8</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>170.3</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>162.4</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>161.4</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>166.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>166.1</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>183.9</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>167.4</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>156.5</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>163.1</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>170.2</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>151.6</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>165.1</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>176.4</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>160.6</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>154.8</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>161.9</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>154.2</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>160.2</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>167.1</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>175.5</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>161.1</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>170.5</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>159.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>178.9</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>151.2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>164.7</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>160.1</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>158.9</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>177.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>166.6</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>166.8</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>193.9</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>160.3</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Height  Weight\n",
       "0     158.9    46.0\n",
       "1     166.5    70.0\n",
       "2     188.9    89.0\n",
       "3       NaN    41.0\n",
       "4     174.0    74.0\n",
       "5     158.0    51.0\n",
       "6     162.5    52.0\n",
       "7     161.9    50.0\n",
       "8     163.0    48.0\n",
       "9     164.8     NaN\n",
       "10    174.1    74.0\n",
       "11    153.2    43.0\n",
       "12      NaN    48.0\n",
       "13    160.1    48.0\n",
       "14    165.3    57.0\n",
       "15    156.0    49.0\n",
       "16    170.1    68.0\n",
       "17    162.8    65.0\n",
       "18    170.3    71.0\n",
       "19    162.4    50.0\n",
       "20    161.4    47.0\n",
       "21    166.0    62.0\n",
       "22    166.1    55.0\n",
       "23    183.9    87.0\n",
       "24    167.4    69.0\n",
       "25    156.5    42.0\n",
       "26      NaN    48.0\n",
       "27    163.1    53.0\n",
       "28    170.2    63.0\n",
       "29    151.6    43.0\n",
       "..      ...     ...\n",
       "170   165.1    57.0\n",
       "171   176.4    80.0\n",
       "172   160.6    53.0\n",
       "173   154.8    44.0\n",
       "174   161.9    54.0\n",
       "175   154.2    41.0\n",
       "176   160.2    51.0\n",
       "177   167.1    71.0\n",
       "178     NaN    76.0\n",
       "179   175.5    73.0\n",
       "180   161.1    50.0\n",
       "181     NaN    55.0\n",
       "182   170.5    73.0\n",
       "183   159.9    46.0\n",
       "184   178.9    80.0\n",
       "185   151.2    43.0\n",
       "186     NaN    53.0\n",
       "187   164.7    51.0\n",
       "188   160.1    53.0\n",
       "189   158.9    47.0\n",
       "190   177.3     NaN\n",
       "191   166.6    54.0\n",
       "192   166.8    70.0\n",
       "193   193.9    79.0\n",
       "194   160.3    49.0\n",
       "195   153.9    46.0\n",
       "196   160.9    50.0\n",
       "197   153.9    45.0\n",
       "198   175.3    71.0\n",
       "199   155.7    51.0\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo = df[['Height','Weight']]\n",
    "df_demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Height    163.218033\n",
       "Weight     55.015873\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Height    183\n",
       "Weight    189\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Height    193\n",
       "Weight      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 唯一值函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tsinghua University              69\n",
       "Shanghai Jiao Tong University    57\n",
       "Fudan University                 40\n",
       "Peking University                34\n",
       "Name: School, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['School'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>188.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Gaojuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaoli Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/31</td>\n",
       "      <td>0:03:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/12</td>\n",
       "      <td>0:03:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoqiang Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/3</td>\n",
       "      <td>0:03:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changli Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>163.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Juan Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaopeng Zhou</td>\n",
       "      <td>Male</td>\n",
       "      <td>174.1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/29</td>\n",
       "      <td>0:05:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaoquan Lv</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/16</td>\n",
       "      <td>0:04:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Peng You</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/20</td>\n",
       "      <td>0:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Yanfeng Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/19</td>\n",
       "      <td>0:05:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaomei Zhou</td>\n",
       "      <td>Female</td>\n",
       "      <td>165.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/29</td>\n",
       "      <td>0:05:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang Yang</td>\n",
       "      <td>Female</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/1</td>\n",
       "      <td>0:05:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaoqiang Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>170.1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/11</td>\n",
       "      <td>0:04:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Peng Wang</td>\n",
       "      <td>Male</td>\n",
       "      <td>162.8</td>\n",
       "      <td>65.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/2</td>\n",
       "      <td>0:04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaofeng Sun</td>\n",
       "      <td>Male</td>\n",
       "      <td>170.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/4</td>\n",
       "      <td>0:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Qiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/30</td>\n",
       "      <td>0:03:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Changjuan You</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaopeng Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/2</td>\n",
       "      <td>0:04:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Changqiang Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>166.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/29</td>\n",
       "      <td>0:05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Qiang Zheng</td>\n",
       "      <td>Male</td>\n",
       "      <td>183.9</td>\n",
       "      <td>87.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/5</td>\n",
       "      <td>0:04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chunmei You</td>\n",
       "      <td>Male</td>\n",
       "      <td>167.4</td>\n",
       "      <td>69.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/17</td>\n",
       "      <td>0:04:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaopeng Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>156.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/19</td>\n",
       "      <td>0:04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Yanli You</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/23</td>\n",
       "      <td>0:03:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Qiang Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>163.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/11</td>\n",
       "      <td>0:05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaoqiang Qin</td>\n",
       "      <td>Female</td>\n",
       "      <td>170.2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Changmei Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Li Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>165.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/26</td>\n",
       "      <td>0:04:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaofeng Zhang</td>\n",
       "      <td>Male</td>\n",
       "      <td>176.4</td>\n",
       "      <td>80.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/25</td>\n",
       "      <td>0:05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Quan Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.6</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/4</td>\n",
       "      <td>0:03:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaojuan Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.8</td>\n",
       "      <td>44.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/26</td>\n",
       "      <td>0:03:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaopeng Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/4</td>\n",
       "      <td>0:05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Yanli Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/8</td>\n",
       "      <td>0:03:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaopeng Zhou</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/15</td>\n",
       "      <td>0:04:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoqiang Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>167.1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/11</td>\n",
       "      <td>0:04:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Li Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:05:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Peng Wang</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/3</td>\n",
       "      <td>0:05:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Zheng</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/28</td>\n",
       "      <td>0:03:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Yanjuan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/23</td>\n",
       "      <td>0:03:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaoqiang Qian</td>\n",
       "      <td>Male</td>\n",
       "      <td>170.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/3</td>\n",
       "      <td>0:04:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaofeng Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>159.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:05:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Feng</td>\n",
       "      <td>Male</td>\n",
       "      <td>178.9</td>\n",
       "      <td>80.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/6</td>\n",
       "      <td>0:04:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Chunmei Wang</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/10</td>\n",
       "      <td>0:04:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Yanjuan Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/9</td>\n",
       "      <td>0:04:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/11</td>\n",
       "      <td>0:05:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaopeng Shen</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/16</td>\n",
       "      <td>0:03:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Chunjuan Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>158.9</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/18</td>\n",
       "      <td>0:05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Changli Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>177.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/21</td>\n",
       "      <td>0:03:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Li Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>166.6</td>\n",
       "      <td>54.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/3</td>\n",
       "      <td>0:04:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Gaojuan Wang</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.8</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/23</td>\n",
       "      <td>0:03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Xiaoqiang Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>193.9</td>\n",
       "      <td>79.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Yanmei Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/3</td>\n",
       "      <td>0:05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Fudan University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaojuan Sun</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:04:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Li Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/22</td>\n",
       "      <td>0:04:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengqiang Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>153.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/5</td>\n",
       "      <td>0:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Shanghai Jiao Tong University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chengmei Shen</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Tsinghua University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Chunpeng Lv</td>\n",
       "      <td>Male</td>\n",
       "      <td>155.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/6</td>\n",
       "      <td>0:05:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School      Grade             Name  Gender  \\\n",
       "0    Shanghai Jiao Tong University   Freshman     Gaopeng Yang  Female   \n",
       "1                Peking University   Freshman   Changqiang You    Male   \n",
       "2    Shanghai Jiao Tong University     Senior          Mei Sun    Male   \n",
       "3                 Fudan University  Sophomore     Xiaojuan Sun  Female   \n",
       "4                 Fudan University  Sophomore      Gaojuan You    Male   \n",
       "5              Tsinghua University   Freshman      Xiaoli Qian  Female   \n",
       "6    Shanghai Jiao Tong University   Freshman        Qiang Chu  Female   \n",
       "7              Tsinghua University     Junior    Gaoqiang Qian  Female   \n",
       "8              Tsinghua University   Freshman    Changli Zhang  Female   \n",
       "9                Peking University     Junior          Juan Xu  Female   \n",
       "10   Shanghai Jiao Tong University   Freshman    Xiaopeng Zhou    Male   \n",
       "11             Tsinghua University     Junior      Xiaoquan Lv  Female   \n",
       "12   Shanghai Jiao Tong University     Senior         Peng You  Female   \n",
       "13   Shanghai Jiao Tong University  Sophomore     Yanfeng Qian  Female   \n",
       "14             Tsinghua University     Senior     Xiaomei Zhou  Female   \n",
       "15                Fudan University   Freshman  Changqiang Yang  Female   \n",
       "16             Tsinghua University     Junior    Xiaoqiang Qin    Male   \n",
       "17             Tsinghua University     Junior        Peng Wang    Male   \n",
       "18             Tsinghua University     Senior     Xiaofeng Sun    Male   \n",
       "19   Shanghai Jiao Tong University     Senior        Qiang Chu  Female   \n",
       "20               Peking University     Junior    Changjuan You  Female   \n",
       "21   Shanghai Jiao Tong University     Senior    Xiaopeng Shen    Male   \n",
       "22   Shanghai Jiao Tong University     Senior   Changqiang Sun  Female   \n",
       "23   Shanghai Jiao Tong University     Senior      Qiang Zheng    Male   \n",
       "24             Tsinghua University     Senior      Chunmei You    Male   \n",
       "25             Tsinghua University     Senior     Xiaopeng Chu  Female   \n",
       "26                Fudan University     Junior        Yanli You  Female   \n",
       "27             Tsinghua University     Junior        Qiang Sun  Female   \n",
       "28                Fudan University   Freshman     Gaoqiang Qin  Female   \n",
       "29               Peking University  Sophomore      Changmei Xu  Female   \n",
       "..                             ...        ...              ...     ...   \n",
       "170               Fudan University  Sophomore           Li Sun  Female   \n",
       "171  Shanghai Jiao Tong University     Senior   Xiaofeng Zhang    Male   \n",
       "172  Shanghai Jiao Tong University     Junior        Quan Zhao  Female   \n",
       "173               Fudan University     Junior     Gaojuan Qian  Female   \n",
       "174  Shanghai Jiao Tong University     Junior     Xiaopeng Sun  Female   \n",
       "175            Tsinghua University     Senior      Yanli Zhang  Female   \n",
       "176            Tsinghua University     Junior    Xiaopeng Zhou  Female   \n",
       "177            Tsinghua University     Junior     Gaoqiang Qin    Male   \n",
       "178            Tsinghua University  Sophomore           Li Qin    Male   \n",
       "179            Tsinghua University     Senior        Peng Wang    Male   \n",
       "180            Tsinghua University     Senior        Mei Zheng  Female   \n",
       "181            Tsinghua University  Sophomore      Yanjuan You    Male   \n",
       "182            Tsinghua University  Sophomore   Xiaoqiang Qian    Male   \n",
       "183              Peking University     Junior    Xiaofeng Zhao  Female   \n",
       "184  Shanghai Jiao Tong University   Freshman       Qiang Feng    Male   \n",
       "185              Peking University   Freshman     Chunmei Wang  Female   \n",
       "186               Fudan University   Freshman     Yanjuan Zhao  Female   \n",
       "187               Fudan University     Junior    Xiaojuan Qian  Female   \n",
       "188  Shanghai Jiao Tong University     Junior    Xiaopeng Shen  Female   \n",
       "189               Fudan University     Junior   Chunjuan Zhang  Female   \n",
       "190  Shanghai Jiao Tong University     Junior      Changli Qin    Male   \n",
       "191            Tsinghua University     Junior           Li Sun  Female   \n",
       "192  Shanghai Jiao Tong University     Senior     Gaojuan Wang    Male   \n",
       "193            Tsinghua University     Senior    Xiaoqiang Qin    Male   \n",
       "194              Peking University     Senior      Yanmei Qian  Female   \n",
       "195               Fudan University     Junior     Xiaojuan Sun  Female   \n",
       "196            Tsinghua University     Senior          Li Zhao  Female   \n",
       "197  Shanghai Jiao Tong University     Senior   Chengqiang Chu  Female   \n",
       "198  Shanghai Jiao Tong University     Senior    Chengmei Shen    Male   \n",
       "199            Tsinghua University  Sophomore      Chunpeng Lv    Male   \n",
       "\n",
       "     Height  Weight Transfer  Test_Number   Test_Date Time_Record  \n",
       "0     158.9    46.0        N            1   2019/10/5     0:04:34  \n",
       "1     166.5    70.0        N            1    2019/9/4     0:04:20  \n",
       "2     188.9    89.0        N            2   2019/9/12     0:05:22  \n",
       "3       NaN    41.0        N            2    2020/1/3     0:04:08  \n",
       "4     174.0    74.0        N            2   2019/11/6     0:05:22  \n",
       "5     158.0    51.0        N            1  2019/10/31     0:03:47  \n",
       "6     162.5    52.0        N            1  2019/12/12     0:03:53  \n",
       "7     161.9    50.0        N            1    2019/9/3     0:03:45  \n",
       "8     163.0    48.0        N            1    2020/1/5     0:05:13  \n",
       "9     164.8     NaN        N            3   2019/10/5     0:04:05  \n",
       "10    174.1    74.0        N            1   2019/9/29     0:05:16  \n",
       "11    153.2    43.0        N            2   2019/9/16     0:04:49  \n",
       "12      NaN    48.0      NaN            2  2019/10/20     0:04:10  \n",
       "13    160.1    48.0        N            2   2019/9/19     0:05:29  \n",
       "14    165.3    57.0        N            1  2019/12/29     0:05:25  \n",
       "15    156.0    49.0        N            3    2020/1/1     0:05:25  \n",
       "16    170.1    68.0        N            1   2019/9/11     0:04:51  \n",
       "17    162.8    65.0        N            1   2019/11/2     0:04:53  \n",
       "18    170.3    71.0        N            2   2019/11/4     0:03:32  \n",
       "19    162.4    50.0        N            3   2019/9/30     0:03:36  \n",
       "20    161.4    47.0        N            1   2019/10/5     0:04:08  \n",
       "21    166.0    62.0      NaN            1    2020/1/2     0:04:54  \n",
       "22    166.1    55.0        N            1  2019/11/29     0:05:01  \n",
       "23    183.9    87.0        N            1   2019/12/5     0:04:59  \n",
       "24    167.4    69.0        N            1  2019/11/17     0:04:32  \n",
       "25    156.5    42.0        N            1  2019/11/19     0:04:59  \n",
       "26      NaN    48.0        N            1   2019/9/23     0:03:34  \n",
       "27    163.1    53.0        N            1  2019/12/11     0:05:08  \n",
       "28    170.2    63.0        N            2    2020/1/7     0:05:24  \n",
       "29    151.6    43.0        N            2    2020/1/3     0:04:28  \n",
       "..      ...     ...      ...          ...         ...         ...  \n",
       "170   165.1    57.0        N            2  2019/12/26     0:04:57  \n",
       "171   176.4    80.0        N            1  2019/12/25     0:05:03  \n",
       "172   160.6    53.0        N            2   2019/10/4     0:03:45  \n",
       "173   154.8    44.0        N            3  2019/10/26     0:03:47  \n",
       "174   161.9    54.0        N            2   2019/11/4     0:05:09  \n",
       "175   154.2    41.0        N            3    2020/1/8     0:03:48  \n",
       "176   160.2    51.0      NaN            2  2019/11/15     0:04:57  \n",
       "177   167.1    71.0        N            2  2019/10/11     0:04:14  \n",
       "178     NaN    76.0        N            3    2020/1/7     0:05:19  \n",
       "179   175.5    73.0        N            3   2019/10/3     0:05:14  \n",
       "180   161.1    50.0        N            3  2019/10/28     0:03:42  \n",
       "181     NaN    55.0        N            1  2019/11/23     0:03:50  \n",
       "182   170.5    73.0        N            3   2019/10/3     0:04:11  \n",
       "183   159.9    46.0        N            1  2019/10/17     0:05:20  \n",
       "184   178.9    80.0        N            2   2019/12/6     0:04:23  \n",
       "185   151.2    43.0        N            2  2019/12/10     0:04:24  \n",
       "186     NaN    53.0        N            2   2019/10/9     0:04:21  \n",
       "187   164.7    51.0        N            2  2019/10/11     0:05:11  \n",
       "188   160.1    53.0        N            1  2019/10/16     0:03:33  \n",
       "189   158.9    47.0        N            2   2019/9/18     0:05:09  \n",
       "190   177.3     NaN        N            1  2019/11/21     0:03:57  \n",
       "191   166.6    54.0        N            2    2019/9/3     0:04:45  \n",
       "192   166.8    70.0        N            1  2019/12/23     0:03:54  \n",
       "193   193.9    79.0        N            2   2019/11/6     0:05:09  \n",
       "194   160.3    49.0      NaN            1   2019/12/3     0:05:08  \n",
       "195   153.9    46.0        N            2  2019/10/17     0:04:31  \n",
       "196   160.9    50.0        N            3   2019/9/22     0:04:03  \n",
       "197   153.9    45.0        N            1    2020/1/5     0:04:48  \n",
       "198   175.3    71.0        N            2    2020/1/7     0:04:58  \n",
       "199   155.7    51.0        N            1   2019/11/6     0:05:05  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实践一\n",
    "* 请计算：所有不同学校的身高、体重的均值、最大值、最小值\n",
    "* 请计算：所有不同学校的男女比例情况\n",
    "* 统计：不同学校的 Grade 的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Shanghai Jiao Tong University', 'Peking University',\n",
       "       'Fudan University', 'Tsinghua University'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['School'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* query()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Transfer</th>\n",
       "      <th>Test_Number</th>\n",
       "      <th>Test_Date</th>\n",
       "      <th>Time_Record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changqiang You</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/4</td>\n",
       "      <td>0:04:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Juan Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Changjuan You</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/5</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Changmei Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Changli Lv</td>\n",
       "      <td>Female</td>\n",
       "      <td>148.7</td>\n",
       "      <td>41.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/13</td>\n",
       "      <td>0:04:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaopeng Shi</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/12</td>\n",
       "      <td>0:04:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Gaoli Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>175.4</td>\n",
       "      <td>78.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/8</td>\n",
       "      <td>0:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaojuan Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/10</td>\n",
       "      <td>0:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Han</td>\n",
       "      <td>Male</td>\n",
       "      <td>185.3</td>\n",
       "      <td>87.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2020/1/7</td>\n",
       "      <td>0:03:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Quan Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/28</td>\n",
       "      <td>0:04:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaojuan Chu</td>\n",
       "      <td>Male</td>\n",
       "      <td>162.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/11/29</td>\n",
       "      <td>0:03:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changquan Chu</td>\n",
       "      <td>Female</td>\n",
       "      <td>159.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/9</td>\n",
       "      <td>0:04:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoli Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>157.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/11</td>\n",
       "      <td>0:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaopeng Qin</td>\n",
       "      <td>Male</td>\n",
       "      <td>172.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/23</td>\n",
       "      <td>0:05:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Gaoquan Zhou</td>\n",
       "      <td>Male</td>\n",
       "      <td>166.8</td>\n",
       "      <td>70.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/5</td>\n",
       "      <td>0:04:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Qiang You</td>\n",
       "      <td>Female</td>\n",
       "      <td>170.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/12/31</td>\n",
       "      <td>0:04:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Mei Xu</td>\n",
       "      <td>Female</td>\n",
       "      <td>154.2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/5</td>\n",
       "      <td>0:04:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Feng Zheng</td>\n",
       "      <td>Female</td>\n",
       "      <td>162.6</td>\n",
       "      <td>49.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/11/5</td>\n",
       "      <td>0:04:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Xiaopeng Han</td>\n",
       "      <td>Female</td>\n",
       "      <td>164.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/18</td>\n",
       "      <td>0:05:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changmei Feng</td>\n",
       "      <td>Female</td>\n",
       "      <td>163.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/11/8</td>\n",
       "      <td>0:04:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Changpeng Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>181.3</td>\n",
       "      <td>83.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/10/24</td>\n",
       "      <td>0:04:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Xiaoli Zhou</td>\n",
       "      <td>Female</td>\n",
       "      <td>166.8</td>\n",
       "      <td>55.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/28</td>\n",
       "      <td>0:05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Chengli Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/13</td>\n",
       "      <td>0:03:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Feng Zhao</td>\n",
       "      <td>Male</td>\n",
       "      <td>167.2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2020/1/3</td>\n",
       "      <td>0:04:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Sophomore</td>\n",
       "      <td>Peng Han</td>\n",
       "      <td>Female</td>\n",
       "      <td>147.8</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/9/19</td>\n",
       "      <td>0:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Changquan Han</td>\n",
       "      <td>Male</td>\n",
       "      <td>173.4</td>\n",
       "      <td>77.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/4</td>\n",
       "      <td>0:03:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Mei Feng</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>2019/9/28</td>\n",
       "      <td>0:05:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Chunpeng Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>161.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/10</td>\n",
       "      <td>0:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Qiang Zhang</td>\n",
       "      <td>Female</td>\n",
       "      <td>152.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/11/30</td>\n",
       "      <td>0:05:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Juan You</td>\n",
       "      <td>Male</td>\n",
       "      <td>169.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/31</td>\n",
       "      <td>0:05:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Chengpeng Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>156.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/9/2</td>\n",
       "      <td>0:03:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Xiaofeng Zhao</td>\n",
       "      <td>Female</td>\n",
       "      <td>159.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/10/17</td>\n",
       "      <td>0:05:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Freshman</td>\n",
       "      <td>Chunmei Wang</td>\n",
       "      <td>Female</td>\n",
       "      <td>151.2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>2019/12/10</td>\n",
       "      <td>0:04:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Peking University</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Yanmei Qian</td>\n",
       "      <td>Female</td>\n",
       "      <td>160.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019/12/3</td>\n",
       "      <td>0:05:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                School      Grade            Name  Gender  Height  Weight  \\\n",
       "1    Peking University   Freshman  Changqiang You    Male   166.5    70.0   \n",
       "9    Peking University     Junior         Juan Xu  Female   164.8     NaN   \n",
       "20   Peking University     Junior   Changjuan You  Female   161.4    47.0   \n",
       "29   Peking University  Sophomore     Changmei Xu  Female   151.6    43.0   \n",
       "30   Peking University     Senior      Changli Lv  Female   148.7    41.0   \n",
       "32   Peking University   Freshman     Gaopeng Shi  Female   162.9    48.0   \n",
       "35   Peking University   Freshman      Gaoli Zhao    Male   175.4    78.0   \n",
       "36   Peking University   Freshman    Xiaojuan Qin    Male     NaN    79.0   \n",
       "38   Peking University   Freshman       Qiang Han    Male   185.3    87.0   \n",
       "45   Peking University   Freshman        Quan Chu  Female   154.7    43.0   \n",
       "54   Peking University   Freshman    Xiaojuan Chu    Male   162.4    58.0   \n",
       "57   Peking University   Freshman   Changquan Chu  Female   159.6    45.0   \n",
       "59   Peking University     Junior        Gaoli Xu  Female   157.3    48.0   \n",
       "61   Peking University  Sophomore    Xiaopeng Qin    Male   172.8     NaN   \n",
       "72   Peking University     Junior    Gaoquan Zhou    Male   166.8    70.0   \n",
       "75   Peking University     Junior       Qiang You  Female   170.0    56.0   \n",
       "83   Peking University  Sophomore          Mei Xu  Female   154.2    39.0   \n",
       "86   Peking University     Senior      Feng Zheng  Female   162.6    49.0   \n",
       "88   Peking University   Freshman    Xiaopeng Han  Female   164.1    53.0   \n",
       "96   Peking University   Freshman   Changmei Feng  Female   163.8    56.0   \n",
       "99   Peking University   Freshman  Changpeng Zhao    Male   181.3    83.0   \n",
       "101  Peking University  Sophomore     Xiaoli Zhou  Female   166.8    55.0   \n",
       "102  Peking University     Junior    Chengli Zhao    Male     NaN     NaN   \n",
       "116  Peking University     Senior       Feng Zhao    Male   167.2    66.0   \n",
       "120  Peking University  Sophomore        Peng Han  Female   147.8    34.0   \n",
       "127  Peking University     Senior   Changquan Han    Male   173.4    77.0   \n",
       "130  Peking University     Senior        Mei Feng  Female     NaN    51.0   \n",
       "132  Peking University     Senior   Chunpeng Qian  Female   161.6     NaN   \n",
       "140  Peking University   Freshman     Qiang Zhang  Female   152.7    43.0   \n",
       "147  Peking University     Senior        Juan You    Male   169.2    69.0   \n",
       "159  Peking University     Junior  Chengpeng Zhao  Female   156.0    44.0   \n",
       "183  Peking University     Junior   Xiaofeng Zhao  Female   159.9    46.0   \n",
       "185  Peking University   Freshman    Chunmei Wang  Female   151.2    43.0   \n",
       "194  Peking University     Senior     Yanmei Qian  Female   160.3    49.0   \n",
       "\n",
       "    Transfer  Test_Number   Test_Date Time_Record  \n",
       "1          N            1    2019/9/4     0:04:20  \n",
       "9          N            3   2019/10/5     0:04:05  \n",
       "20         N            1   2019/10/5     0:04:08  \n",
       "29         N            2    2020/1/3     0:04:28  \n",
       "30         N            2  2019/11/13     0:04:54  \n",
       "32         N            1   2019/9/12     0:04:58  \n",
       "35         N            2   2019/10/8     0:03:32  \n",
       "36         Y            1  2019/12/10     0:04:10  \n",
       "38         N            3    2020/1/7     0:03:58  \n",
       "45         N            1  2019/11/28     0:04:47  \n",
       "54         Y            3  2019/11/29     0:03:42  \n",
       "57         N            2   2019/12/9     0:04:18  \n",
       "59         N            2  2019/12/11     0:05:13  \n",
       "61         N            1  2019/12/23     0:05:29  \n",
       "72         N            2    2019/9/5     0:04:24  \n",
       "75         N            3  2019/12/31     0:04:27  \n",
       "83         N            2   2019/11/5     0:04:29  \n",
       "86         N            2   2019/11/5     0:04:11  \n",
       "88         N            1  2019/12/18     0:05:20  \n",
       "96         N            3   2019/11/8     0:04:41  \n",
       "99         N            2  2019/10/24     0:04:08  \n",
       "101        N            1  2019/10/28     0:05:24  \n",
       "102      NaN            1  2019/10/13     0:03:55  \n",
       "116        N            1    2020/1/3     0:04:56  \n",
       "120      NaN            2   2019/9/19     0:03:32  \n",
       "127        N            1   2019/11/4     0:03:56  \n",
       "130        N            3   2019/9/28     0:05:29  \n",
       "132        N            1  2019/11/10     0:04:10  \n",
       "140        N            1  2019/11/30     0:05:27  \n",
       "147      NaN            1  2019/10/31     0:05:28  \n",
       "159        N            1    2019/9/2     0:03:53  \n",
       "183        N            1  2019/10/17     0:05:20  \n",
       "185        N            2  2019/12/10     0:04:24  \n",
       "194      NaN            1   2019/12/3     0:05:08  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\" School ==  'Peking University'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "u1 = df.query(\"School == 'Peking University'\")  # test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "u11 = u1[['Height','Weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>162.977419</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.969530</td>\n",
       "      <td>14.605935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>147.800000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.650000</td>\n",
       "      <td>44.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>162.600000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>68.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>185.300000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height     Weight\n",
       "count   31.000000  30.000000\n",
       "mean   162.977419  55.666667\n",
       "std      8.969530  14.605935\n",
       "min    147.800000  34.000000\n",
       "25%    156.650000  44.250000\n",
       "50%    162.600000  50.000000\n",
       "75%    167.000000  68.250000\n",
       "max    185.300000  87.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u11.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "u2 = df.query(\"School == 'Shanghai Jiao Tong University'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "u22 = u1[['Height','Weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>162.977419</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.969530</td>\n",
       "      <td>14.605935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>147.800000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.650000</td>\n",
       "      <td>44.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>162.600000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>68.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>185.300000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height     Weight\n",
       "count   31.000000  30.000000\n",
       "mean   162.977419  55.666667\n",
       "std      8.969530  14.605935\n",
       "min    147.800000  34.000000\n",
       "25%    156.650000  44.250000\n",
       "50%    162.600000  50.000000\n",
       "75%    167.000000  68.250000\n",
       "max    185.300000  87.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u22.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "u3 = df.query(\"School == 'Fudan University'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "u33 = u1[['Height','Weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>162.977419</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.969530</td>\n",
       "      <td>14.605935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>147.800000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.650000</td>\n",
       "      <td>44.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>162.600000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>68.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>185.300000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height     Weight\n",
       "count   31.000000  30.000000\n",
       "mean   162.977419  55.666667\n",
       "std      8.969530  14.605935\n",
       "min    147.800000  34.000000\n",
       "25%    156.650000  44.250000\n",
       "50%    162.600000  50.000000\n",
       "75%    167.000000  68.250000\n",
       "max    185.300000  87.000000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u22.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "u4 = df.query(\"School == 'Peking University'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "u44 = u1[['Height','Weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>162.977419</td>\n",
       "      <td>55.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.969530</td>\n",
       "      <td>14.605935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>147.800000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.650000</td>\n",
       "      <td>44.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>162.600000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>68.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>185.300000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height     Weight\n",
       "count   31.000000  30.000000\n",
       "mean   162.977419  55.666667\n",
       "std      8.969530  14.605935\n",
       "min    147.800000  34.000000\n",
       "25%    156.650000  44.250000\n",
       "50%    162.600000  50.000000\n",
       "75%    167.000000  68.250000\n",
       "max    185.300000  87.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u44.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Freshman     13\n",
       "Junior        8\n",
       "Senior        8\n",
       "Sophomore     5\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u1['Grade'].value_counts()   #test3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Senior       22\n",
       "Junior       17\n",
       "Freshman     13\n",
       "Sophomore     5\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u2['Grade'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Junior       12\n",
       "Senior       11\n",
       "Freshman      9\n",
       "Sophomore     8\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u3['Grade'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Freshman     13\n",
       "Junior        8\n",
       "Senior        8\n",
       "Sophomore     5\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u4['Grade'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    22\n",
       "Male      12\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u1['Gender'].value_counts()   #test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2807017543859649"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pu1 = 16/(41+16)\n",
    "pu1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    41\n",
       "Male      16\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u2['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2807017543859649"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pu1 = 16/(41+16)\n",
    "pu1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    30\n",
       "Male      10\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u3['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pu1 = 10/(10+30)\n",
    "pu1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    22\n",
       "Male      12\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u4['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35294117647058826"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pu1 = 12/(22+12)\n",
    "pu1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实践"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>排名</td>\n",
       "      <td>排名变化</td>\n",
       "      <td>企业名称</td>\n",
       "      <td>价值（亿元人民币）</td>\n",
       "      <td>价值变化（亿元人民币）</td>\n",
       "      <td>国家</td>\n",
       "      <td>城市</td>\n",
       "      <td>行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>抖音</td>\n",
       "      <td>13400</td>\n",
       "      <td>-10050</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>社交媒体</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>SpaceX</td>\n",
       "      <td>8400</td>\n",
       "      <td>1680</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>航天</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>蚂蚁集团</td>\n",
       "      <td>8000</td>\n",
       "      <td>-2010</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Stripe</td>\n",
       "      <td>4100</td>\n",
       "      <td>-2210</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>Shein</td>\n",
       "      <td>4000</td>\n",
       "      <td>2680</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>币安</td>\n",
       "      <td>3000</td>\n",
       "      <td>2010</td>\n",
       "      <td>马耳他</td>\n",
       "      <td>马耳他</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Databricks</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>大数据</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>2200</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>京东科技</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>数字科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>Checkout.com</td>\n",
       "      <td>1900</td>\n",
       "      <td>870</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-2</td>\n",
       "      <td>菜鸟网络</td>\n",
       "      <td>1800</td>\n",
       "      <td>-470</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>物流</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>-6</td>\n",
       "      <td>Canva</td>\n",
       "      <td>1750</td>\n",
       "      <td>-940</td>\n",
       "      <td>澳大利亚</td>\n",
       "      <td>悉尼</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>-3</td>\n",
       "      <td>Revolut</td>\n",
       "      <td>1650</td>\n",
       "      <td>-540</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>New</td>\n",
       "      <td>Citadel Securities</td>\n",
       "      <td>1500</td>\n",
       "      <td>New</td>\n",
       "      <td>美国</td>\n",
       "      <td>芝加哥</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>BYJU’s</td>\n",
       "      <td>1500</td>\n",
       "      <td>70</td>\n",
       "      <td>印度</td>\n",
       "      <td>班加罗尔</td>\n",
       "      <td>教育科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>94</td>\n",
       "      <td>极星</td>\n",
       "      <td>1300</td>\n",
       "      <td>1010</td>\n",
       "      <td>瑞典</td>\n",
       "      <td>哥德堡</td>\n",
       "      <td>新能源汽车</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>极兔速递</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>印度尼西亚</td>\n",
       "      <td>雅加达</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>小红书</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>-3</td>\n",
       "      <td>FTX</td>\n",
       "      <td>1300</td>\n",
       "      <td>-340</td>\n",
       "      <td>巴哈马</td>\n",
       "      <td>拿索</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>-9</td>\n",
       "      <td>Instacart</td>\n",
       "      <td>1320</td>\n",
       "      <td>-1270</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>快递</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>-8</td>\n",
       "      <td>Chime</td>\n",
       "      <td>1250</td>\n",
       "      <td>-400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>-2</td>\n",
       "      <td>大疆</td>\n",
       "      <td>1200</td>\n",
       "      <td>130</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>机器人</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22</td>\n",
       "      <td>-3</td>\n",
       "      <td>Lineage Logistics</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>Novi</td>\n",
       "      <td>物流</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>New</td>\n",
       "      <td>Miro</td>\n",
       "      <td>1170</td>\n",
       "      <td>New</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>企业服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>85</td>\n",
       "      <td>联影医疗</td>\n",
       "      <td>1040</td>\n",
       "      <td>700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>健康科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>74</td>\n",
       "      <td>CloudKitchens</td>\n",
       "      <td>1000</td>\n",
       "      <td>640</td>\n",
       "      <td>美国</td>\n",
       "      <td>洛杉矶</td>\n",
       "      <td>共享经济</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>26</td>\n",
       "      <td>-5</td>\n",
       "      <td>Discord</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>社交媒体</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>26</td>\n",
       "      <td>-5</td>\n",
       "      <td>元气森林</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>食品饮料</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26</td>\n",
       "      <td>-5</td>\n",
       "      <td>Gopuff</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>费城</td>\n",
       "      <td>快递</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>71</td>\n",
       "      <td>New</td>\n",
       "      <td>The CrownX</td>\n",
       "      <td>550</td>\n",
       "      <td>New</td>\n",
       "      <td>越南</td>\n",
       "      <td>胡志明市</td>\n",
       "      <td>消费品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>109</td>\n",
       "      <td>Ramp</td>\n",
       "      <td>540</td>\n",
       "      <td>280</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>73</td>\n",
       "      <td>-8</td>\n",
       "      <td>Tempus</td>\n",
       "      <td>540</td>\n",
       "      <td>10</td>\n",
       "      <td>美国</td>\n",
       "      <td>芝加哥</td>\n",
       "      <td>生物科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>New</td>\n",
       "      <td>Dunamu</td>\n",
       "      <td>535</td>\n",
       "      <td>New</td>\n",
       "      <td>韩国</td>\n",
       "      <td>首尔</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>75</td>\n",
       "      <td>704</td>\n",
       "      <td>SumUp</td>\n",
       "      <td>535</td>\n",
       "      <td>470</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>75</td>\n",
       "      <td>293</td>\n",
       "      <td>StarkWare</td>\n",
       "      <td>535</td>\n",
       "      <td>400</td>\n",
       "      <td>以色列</td>\n",
       "      <td>内坦亚</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>75</td>\n",
       "      <td>137</td>\n",
       "      <td>飞协博</td>\n",
       "      <td>535</td>\n",
       "      <td>320</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>物流</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "      <td>Dream11</td>\n",
       "      <td>535</td>\n",
       "      <td>200</td>\n",
       "      <td>印度</td>\n",
       "      <td>孟买</td>\n",
       "      <td>游戏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>75</td>\n",
       "      <td>-10</td>\n",
       "      <td>Fireblocks</td>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>网络安全</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>75</td>\n",
       "      <td>-10</td>\n",
       "      <td>平安智慧城市</td>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>大数据</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>82</td>\n",
       "      <td>-17</td>\n",
       "      <td>Hopin</td>\n",
       "      <td>520</td>\n",
       "      <td>-10</td>\n",
       "      <td>英国</td>\n",
       "      <td>伦敦</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>27</td>\n",
       "      <td>京东产发</td>\n",
       "      <td>515</td>\n",
       "      <td>180</td>\n",
       "      <td>中国</td>\n",
       "      <td>宿迁</td>\n",
       "      <td>企业服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>84</td>\n",
       "      <td>-15</td>\n",
       "      <td>Argo AI</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>哈里斯堡</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>84</td>\n",
       "      <td>-47</td>\n",
       "      <td>滴滴货运</td>\n",
       "      <td>500</td>\n",
       "      <td>-170</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>84</td>\n",
       "      <td>-47</td>\n",
       "      <td>Digital Currency Group</td>\n",
       "      <td>500</td>\n",
       "      <td>-170</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>区块链</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>84</td>\n",
       "      <td>-47</td>\n",
       "      <td>Figma</td>\n",
       "      <td>500</td>\n",
       "      <td>-170</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>-19</td>\n",
       "      <td>Carta</td>\n",
       "      <td>495</td>\n",
       "      <td>-10</td>\n",
       "      <td>美国</td>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>21</td>\n",
       "      <td>TripActions</td>\n",
       "      <td>490</td>\n",
       "      <td>150</td>\n",
       "      <td>美国</td>\n",
       "      <td>帕洛阿尔托</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>89</td>\n",
       "      <td>-20</td>\n",
       "      <td>Scale AI</td>\n",
       "      <td>490</td>\n",
       "      <td>-10</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>-42</td>\n",
       "      <td>Klaviyo</td>\n",
       "      <td>480</td>\n",
       "      <td>-160</td>\n",
       "      <td>美国</td>\n",
       "      <td>波士顿</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>91</td>\n",
       "      <td>-42</td>\n",
       "      <td>OutSystems</td>\n",
       "      <td>480</td>\n",
       "      <td>-160</td>\n",
       "      <td>美国</td>\n",
       "      <td>波士顿</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>91</td>\n",
       "      <td>-42</td>\n",
       "      <td>ServiceTitan</td>\n",
       "      <td>480</td>\n",
       "      <td>-160</td>\n",
       "      <td>美国</td>\n",
       "      <td>格兰岱尔市</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>91</td>\n",
       "      <td>-42</td>\n",
       "      <td>OYO</td>\n",
       "      <td>480</td>\n",
       "      <td>-160</td>\n",
       "      <td>印度</td>\n",
       "      <td>古尔冈</td>\n",
       "      <td>共享经济</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>117</td>\n",
       "      <td>ConsenSys</td>\n",
       "      <td>470</td>\n",
       "      <td>250</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>软件服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>95</td>\n",
       "      <td>15</td>\n",
       "      <td>Ro</td>\n",
       "      <td>470</td>\n",
       "      <td>130</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>健康科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>95</td>\n",
       "      <td>-16</td>\n",
       "      <td>Impossible 食品</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "      <td>美国</td>\n",
       "      <td>雷德伍德城</td>\n",
       "      <td>食品饮料</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>95</td>\n",
       "      <td>-16</td>\n",
       "      <td>微医</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>健康科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>58</td>\n",
       "      <td>蜂巢能源</td>\n",
       "      <td>460</td>\n",
       "      <td>190</td>\n",
       "      <td>中国</td>\n",
       "      <td>常州</td>\n",
       "      <td>新能源</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>99</td>\n",
       "      <td>-6</td>\n",
       "      <td>Better.com</td>\n",
       "      <td>460</td>\n",
       "      <td>60</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>金融科技</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>99</td>\n",
       "      <td>-20</td>\n",
       "      <td>Automation Anywhere</td>\n",
       "      <td>460</td>\n",
       "      <td>-10</td>\n",
       "      <td>美国</td>\n",
       "      <td>圣何塞</td>\n",
       "      <td>人工智能</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1                       2          3            4      5      6  \\\n",
       "0    排名  排名变化                    企业名称  价值（亿元人民币）  价值变化（亿元人民币）     国家     城市   \n",
       "1     1     0                      抖音      13400       -10050     中国     北京   \n",
       "2     2     1                  SpaceX       8400         1680     美国    洛杉矶   \n",
       "3     3    -1                    蚂蚁集团       8000        -2010     中国     杭州   \n",
       "4     4     0                  Stripe       4100        -2210     美国    旧金山   \n",
       "5     5    11                   Shein       4000         2680     中国     广州   \n",
       "6     6    15                      币安       3000         2010    马耳他    马耳他   \n",
       "7     7     1              Databricks       2500            0     美国    旧金山   \n",
       "8     8     3                    微众银行       2200          200     中国     深圳   \n",
       "9     9     2                    京东科技       2000            0     中国     北京   \n",
       "10   10    11            Checkout.com       1900          870     英国     伦敦   \n",
       "11   11    -2                    菜鸟网络       1800         -470     中国     杭州   \n",
       "12   12    -6                   Canva       1750         -940   澳大利亚     悉尼   \n",
       "13   13    -3                 Revolut       1650         -540     英国     伦敦   \n",
       "14   14   New      Citadel Securities       1500          New     美国    芝加哥   \n",
       "15   14     1                  BYJU’s       1500           70     印度   班加罗尔   \n",
       "16   16    94                      极星       1300         1010     瑞典    哥德堡   \n",
       "17   16     0                    极兔速递       1300            0  印度尼西亚    雅加达   \n",
       "18   16     0                     小红书       1300            0     中国     上海   \n",
       "19   16    -3                     FTX       1300         -340    巴哈马     拿索   \n",
       "20   16    -9               Instacart       1320        -1270     美国    旧金山   \n",
       "21   21    -8                   Chime       1250         -400     美国    旧金山   \n",
       "22   22    -2                      大疆       1200          130     中国     深圳   \n",
       "23   22    -3       Lineage Logistics       1200            0     美国   Novi   \n",
       "24   24   New                    Miro       1170          New     美国    旧金山   \n",
       "25   25    85                    联影医疗       1040          700     中国     上海   \n",
       "26   26    74           CloudKitchens       1000          640     美国    洛杉矶   \n",
       "27   26    -5                 Discord       1000            0     美国    旧金山   \n",
       "28   26    -5                    元气森林       1000            0     中国     北京   \n",
       "29   26    -5                  Gopuff       1000            0     美国     费城   \n",
       "..   ..   ...                     ...        ...          ...    ...    ...   \n",
       "72   71   New              The CrownX        550          New     越南   胡志明市   \n",
       "73   73   109                    Ramp        540          280     美国     纽约   \n",
       "74   73    -8                  Tempus        540           10     美国    芝加哥   \n",
       "75   75   New                  Dunamu        535          New     韩国     首尔   \n",
       "76   75   704                   SumUp        535          470     英国     伦敦   \n",
       "77   75   293               StarkWare        535          400    以色列    内坦亚   \n",
       "78   75   137                     飞协博        535          320     美国    旧金山   \n",
       "79   75    35                 Dream11        535          200     印度     孟买   \n",
       "80   75   -10              Fireblocks        535            0     美国     纽约   \n",
       "81   75   -10                  平安智慧城市        535            0     中国     深圳   \n",
       "82   82   -17                   Hopin        520          -10     英国     伦敦   \n",
       "83   83    27                    京东产发        515          180     中国     宿迁   \n",
       "84   84   -15                 Argo AI        500            0     美国   哈里斯堡   \n",
       "85   84   -47                    滴滴货运        500         -170     中国     北京   \n",
       "86   84   -47  Digital Currency Group        500         -170     美国     纽约   \n",
       "87   84   -47                   Figma        500         -170     美国    旧金山   \n",
       "88   88   -19                   Carta        495          -10     美国  帕洛阿尔托   \n",
       "89   89    21             TripActions        490          150     美国  帕洛阿尔托   \n",
       "90   89   -20                Scale AI        490          -10     美国    旧金山   \n",
       "91   91   -42                 Klaviyo        480         -160     美国    波士顿   \n",
       "92   91   -42              OutSystems        480         -160     美国    波士顿   \n",
       "93   91   -42            ServiceTitan        480         -160     美国  格兰岱尔市   \n",
       "94   91   -42                     OYO        480         -160     印度    古尔冈   \n",
       "95   95   117               ConsenSys        470          250     美国     纽约   \n",
       "96   95    15                      Ro        470          130     美国     纽约   \n",
       "97   95   -16           Impossible 食品        470            0     美国  雷德伍德城   \n",
       "98   95   -16                      微医        470            0     中国     杭州   \n",
       "99   99    58                    蜂巢能源        460          190     中国     常州   \n",
       "100  99    -6              Better.com        460           60     美国     纽约   \n",
       "101  99   -20     Automation Anywhere        460          -10     美国    圣何塞   \n",
       "\n",
       "         7  \n",
       "0       行业  \n",
       "1     社交媒体  \n",
       "2       航天  \n",
       "3     金融科技  \n",
       "4     金融科技  \n",
       "5     电子商务  \n",
       "6      区块链  \n",
       "7      大数据  \n",
       "8     金融科技  \n",
       "9     数字科技  \n",
       "10    金融科技  \n",
       "11      物流  \n",
       "12    软件服务  \n",
       "13    金融科技  \n",
       "14    金融科技  \n",
       "15    教育科技  \n",
       "16   新能源汽车  \n",
       "17    电子商务  \n",
       "18    软件服务  \n",
       "19     区块链  \n",
       "20      快递  \n",
       "21    金融科技  \n",
       "22     机器人  \n",
       "23      物流  \n",
       "24    企业服务  \n",
       "25    健康科技  \n",
       "26    共享经济  \n",
       "27    社交媒体  \n",
       "28    食品饮料  \n",
       "29      快递  \n",
       "..     ...  \n",
       "72     消费品  \n",
       "73    金融科技  \n",
       "74    生物科技  \n",
       "75     区块链  \n",
       "76    金融科技  \n",
       "77     区块链  \n",
       "78      物流  \n",
       "79      游戏  \n",
       "80    网络安全  \n",
       "81     大数据  \n",
       "82    软件服务  \n",
       "83    企业服务  \n",
       "84    人工智能  \n",
       "85      物流  \n",
       "86     区块链  \n",
       "87    软件服务  \n",
       "88    金融科技  \n",
       "89    电子商务  \n",
       "90    人工智能  \n",
       "91    软件服务  \n",
       "92    软件服务  \n",
       "93    软件服务  \n",
       "94    共享经济  \n",
       "95    软件服务  \n",
       "96    健康科技  \n",
       "97    食品饮料  \n",
       "98    健康科技  \n",
       "99     新能源  \n",
       "100   金融科技  \n",
       "101   人工智能  \n",
       "\n",
       "[102 rows x 8 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "hurun_独角兽 = pd.read_html('https://www.hurun.net/zh-CN/Info/Detail?num=L9SQPH9FKJB1')[-3]\n",
    "hurun_独角兽"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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